Apparatus, system, and method for monitoring physiological signs

ABSTRACT

An apparatus, system, and method monitors the motion, breathing, heart rate and sleep state of subjects, e.g., humans, in a convenient, non-invasive/non-contact, and low-cost fashion. More particularly, the motion, breathing, and heart rate signals are obtained through processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Periods of sleep disturbed respiration, or central apnea can be detected through analysis of the respiratory signal. The mean heart rate, and derived information, such as the presence of cardiac arrhythmias can be determined from the cardiac signal. Motion estimates can be used to recognize disturbed sleep and periodic limb movements. The sleep state may be determined by applying a classifier model to the resulting streams of respiratory, cardiac and motion data. A means for display of the sleep state, respiratory, cardiac, and movement status may also be provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/029,423, filed Sep. 17, 2013, now U.S. Pat. No. 10,729,332, which isa continuation of U.S. patent application Ser. No. 12/302,704, filedNov. 26, 2008, now U.S. Pat. No. 8,562,523, which is a national phaseentry under 35 U.S.C. § 371 of International Application No.PCT/US2007/70196, filed Jun. 1, 2007, published in English, which claimspriority from U.S. Provisional Patent Application No. 60/803,657, filedJun. 1, 2006, all of which are hereby incorporated herein by reference.

BACKGROUND OF THE INVENTION

This disclosure relates to the monitoring of motion, breathing, heartrate and sleep state of humans in a convenient and low-cost fashion, andmore particularly to an apparatus, system, and method for acquiring,processing and displaying the corresponding information in a easilyunderstandable format.

Monitoring of sleep patterns, heart rate and respiration during sleep isof interest for many reasons from clinical monitoring of obstructive andcentral sleep apnea in both adults and young children, to ensuringhealthy sleep patterns in young babies. For example, infants which areborn prematurely often have immature cardiorespiratory control which cancause them to stop breathing for 15-20 seconds, or to breathe shallowly.This is referred to as apnea of prematurity, and often persists for twoto three months after birth. Periodic breathing (in which the amplitudeof respiration rises and falls over several minutes) is also common inbabies born prematurely. In such infants, it is also useful to monitorheart rate as a low heart rate (bradycardia) can be used as a warningsignal that the baby is not receiving sufficient oxygen.

In adults, common sleep disordered breathing syndromes includeobstructive sleep apnea and central sleep apnea. In obstructive sleepapnea, the upper airway collapses, restricting the flow of air to thelungs, even in the presence of ongoing respiratory effort. Obstructivesleep apnea can also cause characteristic changes in heart rate, whichmay be detrimental to the subject. Obstructive sleep apnea has a highprevalence in the adult population, affecting about 2-4% of adults overthe age of 40. Obstructive events lead to a reduced flow of air to thelungs, and subsequently a lowering of oxygen level in the blood. Centralsleep apnea is less common than obstructive sleep apnea in adults, andis distinguished by a complete loss of respiratory effort, which leadsto a loss of air to the lungs, and eventually a lowering of oxygen inthe blood. In both central and obstructive sleep apnea, the body'snatural defense mechanisms will be stimulated by the oxygendesaturation, and eventually increase respiratory effort sufficient torestore airflow. However, this is often accompanied by an arousal (whichcan be observed in the person's electroencephalogram) which either wakesthe person up momentarily, or brings them into a lighter stage of sleep.In either event, the person's sleep is disrupted, and they experiencepoor quality sleep, which often leads to excessive daytime sleepiness.

Other common sleep disorders in adults, whose effects are not related torespiration are Periodic Limb Movements Disorder (PLMD) and RestlessLegs Syndrome (RLS). In PLMD, a subject makes characteristic repetitivemovements (usually of the leg) every 30-40 seconds, leading to sleepdisruption due to frequent awakenings. In RLS, the subject has anoverwhelming desire to move or flex their legs as they fall asleep,again leading to disrupted sleep patterns. Monitoring of these unusualbody movements is important to confirming the diagnosis of theseconditions and initiating treatment.

The most common adult sleep disorder is insomnia which is defined as adifficulty in initiating or maintaining sleep. Chronic insomnia isestimated to affect about 10% of the American population. However, atpresent full clinical evaluation of sleep patterns relies onelectroencephalograph (EEG) monitoring, often requiring a hospital stay.There is a need for simpler methods of assessing sleep patterns foradults in the home environment. For example, evidence has shown thatsleep deprivation adversely alters the balance of leptin and ghrelin,two hormones which are significantly involved with the body's appetitecontrol system. Voluntary sleep deprivation over a period of time (dueto lifestyle choice) has been correlated with increased Body-Mass-Index(an indicator of obesity). Hence, objective measurement and control ofsleep patterns may play a role in weight management.

Moreover, sleep is of particular important to young children. Infantsspend more time asleep than awake in their first three years,emphasizing its crucial importance in development. Sleep is importantfor physical recuperation, growth, maturing of the immune system, braindevelopment, learning, and memory. Conversely, infants who do notreceive sufficient sleep or who sleep poorly often display poor mood, aswell as having an adverse effect on their parents' sleep patterns.Indeed it is estimated that 20-30% of children under the age of 3 yearshave common sleep problems such as frequent night-wakings, anddifficulty falling asleep on their own. Studies have shown that parentscan help their babies achieve good sleep patterns through a variety ofbehavioral approaches. A non-invasive safe sleep monitor can assist inadopting such behavioral approaches. Automated collection of sleepinformation can help parents in assuring their children are sleepingadequately. For example, a system which monitors night-time sleep anddaytime naps can provide information in the form of a visual sleep logwhich can be stored and visualized over a period of time (e.g., using aworld wide web interface on a personalized page). The sleep monitor canalso track sleep fragmentation (e.g., frequent awakenings duringnight-time sleep), which is correlated with infant contentment. Finally,characteristic changes in breathing, heart rate, and movement may beassociated with night-time urination and defecation in infants, andhence can be used to alert parents to change diapers.

In adults, measurements of heart rate and breathing rate during sleepcan be used as clinical markers for continuous health monitoring. Forexample, elevated breathing rates can be linked to forms of respiratorydistress or diseases such as chronic obstructive pulmonary disease whichrequire increased respiratory effort. It has been shown in clinicalstudies that a particular type of breathing pattern, referred to asCheyne-Stokes respiration or periodic breathing, is a marker for poorprognosis in people with heart disease. Simultaneous measurement ofrespiration and cardiac activity can also allow evaluation of aphenomenon called respiratory sinus arrhythmia (RSA) in which the heartrate speeds up and slows down in response to each breath. The amplitudeof this coupling effect is typically stronger in young healthy people,and therefore can be used as another health marker. Heart rate changesduring sleep can also provide useful clinical information—elevated heartrates can be an indicator of systemic activation of the sympatheticnervous system, which can be associated with sleep apnea or otherconditions. Furthermore, a common clinical problem is to monitorresponse to treatments aimed at stabilizing heart rhythm. For example, acommon cardiac arrhythmia is atrial fibrillation (AF), in which theupper chambers of the heart beat irregularly. Consequently the heartrate is irregular and elevated. Common treatments for AF includepharmacological and surgical approaches, and a goal of the doctor is toprovide follow-up monitoring to look for a reoccurrence of thearrhythmia. Non-invasive low-cost monitoring of heart rate during sleepis a useful mechanism to provide doctors with a means of providing suchmonitoring follow-up for this condition, and other cardiac arrhythmias.

Accordingly, a method, system or apparatus which can reliably monitorsleep patterns, breathing and heart rate during sleep, and motion duringsleep would have utility in a variety of settings.

A variety of techniques have been disclosed in the background art foraddressing the need for respiratory, cardiac and sleep monitoring.Respiratory monitoring is currently carried out primarily in a hospitalenvironment using a variety of approaches. A common method for measuringrespiratory effort uses inductance plethysmography, in which a personwears a tightly fitting elastic band around their thorax, whoseinductance changes as the person breathes in and out. This technique hasbecome the most widely used respiration monitoring technique in sleepmedicine. A severe limitation of the method from a convenience point ofview is that the person has to wear a band, and remains connected to theassociated electronic recording device via wires.

An alternative system for measuring respiratory effort is to useimpedance pneumography, in which the impedance change of the thorax ismeasured. This technique is often used in clinical infant apneamonitors, which generate an alarm in a baby monitor when no breathing isdetected. In order to detect the breathing signal, electrodes must beattached to a sleeping infant. More generally, there are a number ofcommercial products available which use impedance measurements acrossthe baby's chest to detect central apnea (e.g., the AmiPlus Infant ApneaMonitor produced and marketed by CAS Medical Systems). The limitation ofthis technology is that it requires electrodes to be attached to thebaby, has an active electrical component, and needs to be used withcaution as the wires can cause strangulation if not properly fitted.

Heart rate during sleep can be measured using conventional surfaceelectrocardiogram measurements (typically referred to as a Holtermonitor), in which a person typically wears three or more electrodes. Alimitation of this method is the need to wear electrodes and theassociated electronic recording device. Heart rate fitness monitorsrecord heart rate by also measuring surface electrocardiogram, typicallyusing a wearable chest band which has integrated electrodes. Again,there is the need to wear the device and also the accompanying signalcollector (typically a wrist watch style device). Heart rate duringsleep can also be measured using pulse oximetry, in which aphotoplethysmogram is collected at the finger or ear. There is acharacteristic variation in the pulse photoplethysmogram signal whichcorresponds to each beat of the heart.

Integrated systems for collecting heart rate and respiration usingcombinations of the techniques discussed above for heart rate andrespiratory effort have been developed. In one commercial product,contact ECG and inductance plethysmograph sensors have been embedded ina custom-designed jacket. The cost of providing such a wearable systemis relatively high, and the system requires contact sensors.

One indicator of sleep status is the degree of motion while lying down.Motion during sleep can be detected by wrist-worn accelerometers, suchas those commercially marketed by MiniMitter as “Actiwatch®”. These usemicroelectronic accelerometers to record limb movement during sleep. Alimitation of this technology is the requirement for the individual towear a device, and the fact that it is not integrated with simultaneousbreathing and cardiac monitoring, which limits the physiologicalusefulness of such measurements. Motion can also be detected usingunder-mattress piezoelectric sensors, which produce a voltage spike whenpressure is applied to the mat, and hence can detect movement.

Various approaches to measuring heart rate, respiration, and motion in anon-contact fashion have been described. One approach is to use opticalinterferometry to provide a non-contact method for determiningrespiration, cardiac activity and motion. However, a limitation of theirinvention is that the optical signals are blocked by clothes or beddingmaterials. The processing required to obtain and differentiatebreathing, cardiac and motion elements is unclear. A second approach isto use ultrasonic waves to detect motion. A limitation of this approachis that signal-to-noise ratio can be poor due to low reflection, andrespiration, motion and cardiac signals can not be collectedsimultaneously. A further non-contact measurement technique forassessing bodily motion is to use continuous wave radar (usingelectromagnetic radiation in the radio frequency range) in detectingrespiration and heartbeat.

Limitations of previous methods to obtain physiological data using thesenon-contact methods include various sensor limitations (e.g.,obstruction by bed clothes, poor signal-to-noise ratios, or the need fortoo large an antenna). Furthermore, the background art does not providemethods for extracting useful “higher-level” physiological status, suchas breathing rate, cardiac rhythm status, sleep state, respiratorydistress, or evidence of sleep disturbed breathing. The currentdisclosure also possesses advantages related to the fact that itrequires very low levels of transmitted radio-frequency power (e.g.,less than 0 dBm), can be made in a small size (e.g., the sensor can be 5cm.×5 cm.×5 cm or less in size), can be battery powered, and is safe forhuman use.

BRIEF SUMMARY OF THE INVENTION

This disclosure provides various embodiments of an apparatus, system,and method for monitoring of motion, breathing, heart rate and sleepstate of humans in a convenient and low-cost fashion. In variousembodiments, a sensor unit suitable for being placed close to where thesubject is sleeping (e.g., on a bedside table) may be interfaced with amonitoring and display unit where results can be analyzed, visualizedand communicated to the user. The sensor unit and the display/monitoringunit can be incorporated into a single stand-alone unit, if desired. Theunit may include one or more of a non-contact motion sensor (fordetection of general bodily movement, respiration, and heart rate); aprocessing capability (to derive parameters such as sleep state,breathing rate, heart rate, and movement); a display capability (toprovide visual feedback); an auditory capability (to provide acousticfeedback, e.g., a tone whose frequency varies with breathing, or analarm which sounds when no motion is detected); a communicationscapability (wired or wireless) to transmit acquired data to a separateunit. This separate unit can carry out the processing, display andauditory capability mentioned above, and can also be a data logger.

In one embodiment, an apparatus useful in detecting, analyzing, anddisplaying one or more of respiration, cardiac activity, and bodilyfunction or movement of a subject, may include a processor configured toanalyze a signal reflected from the subject without physical contactwith the subject and to derive measurements of said one or more ofrespiration, cardiac activity, and bodily function or movementtherefrom; and a display configured to provide the analyzed and derivedmeasurements to a local or remote user of the apparatus.

In another embodiment, a system for measuring, analyzing, and displayingone or more of a respiration parameter, cardiac activity, and bodilymovement or function of a subject may include a transmitter arrangementconfigured to propagate a radio frequency signal toward the subject; areceiver arranged to receive a radio-frequency signal reflected from thesubject; a processor arranged to analyze the reflected signal to producemeasurements of one or more of a respiration parameter, cardiacactivity, and a bodily movement or function, and a monitor to provideselected information to a local or remote user of the system by eitheran audible or visual indication, or both.

In another embodiment, a method for measuring, analyzing, and displayingone or more physiological parameters of a subject may include the stepsof sensing a signal reflected from the subject; processing and analyzingthe reflected signal; deriving said one or more physiological parameterspertaining to said subject, said one or more physiological parameterscomprising one or more of a respiration parameter, cardiac activity, andbodily movement or function of a subject; and making selected derivedinformation available to a user.

Additional sensing capabilities may be added to the sensor unit,including a sound sensor; a sensor for measuring body temperature from adistance (infrared); and sensors for environment humidity, temperatureand light level.

The processing capability extracts information relating specifically tothe separate breathing, heart rate, and motion components, and uses thisraw information to derive higher level information such as sleep state,presence of sleep disordered breathing, cardiac arrhythmias, and sleepdisturbance. The display capability provides a means for clearlycommunicating this physiological information in a clearly understandablefashion, such as providing a simple color indicator to indicate sleepstatus (awake or asleep). The processing capability can also incorporatemeasurements from the auxiliary sensors, which allows the derivation ofphysiological information about coughing, wheezing, and otherrespiratory disturbances.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure will now be described with reference tothe accompanying drawings in which:

FIG. 1 is a diagram illustrating a schematic of the radio frequencysensor components of the system, with a pulsed continuous wave signalfor illustration;

FIG. 2 is a diagram illustrating a schematic of how a raw sensor signalcan be processed to produce three signals for further processing;

FIG. 3 is a diagram illustrating a more detailed view of a way by whichthe raw sensor signal can be processed to yield motion information;

FIG. 4 is a diagram illustrating sample signals acquired from the systemfor respiratory activity, in comparison with the signals obtained from aconventional standard inductance plethysmography (using a commercialsystem called Respiband®);

FIG. 5 is a diagram illustrating sample signals acquired from the systemfor cardiac activity in comparison with the signals obtained from anconventional heart rate monitoring system based on a pulse oximeter;

FIG. 6 is a diagram illustrating techniques by which the system maycalculate heart rate;

FIG. 7 is a diagram illustrating how information may be integrated fromthe derived motion m(t), respiratory r(t) and cardiac signals c(t)together to extract meaningful physiological classifications, by using aclassifier model;

FIG. 8 is a diagram illustrating an example of an output displayed inone embodiment;

FIG. 9 is a diagram illustrating how the apparatus and system of thisdisclosure can be used in a wireless communications configuration wherethe processing and display unit are remote from the sensor unit;

FIG. 10 is a diagram illustrating how information may be integrated fromthe derived motion m(t), respiratory r(t) and cardiac signals c(t)together to extract an Apnoea-Hypopnea index (AHI) by using a classifiermodel;

FIG. 11 is a diagram illustrating an algorithm for processing anycombination of the breathing signal, heart-rate and movement signal toform an estimated AHI including using only measured and/or derivedrespiratory effort of a human subject;

FIG. 12 illustrates an example output of the epoch labels of apneaestimated from the breathing signal from a night time recording of asubject for which the estimated AHI was 2.9 and the expert determinedAHI was 4;

FIG. 13 is a block diagram of another embodiment of the apparatus andsystem of this disclosure illustrating auxiliary sensors; and

FIG. 14 provides a non-contact sensor recording for Record Number 2 (topaxis) with the actimetry recording on the bottom axis in which thesignals have been aligned and truncated, and in which the middle axisshows the non-contact signal mapped to actimetry.

DETAILED DESCRIPTION

FIG. 1 is a diagram illustrating a schematic of the radio frequencysensor components of the apparatus and system, with a pulsed continuouswave signal for illustration. The transmitter transmits aradio-frequency signal towards a subject, e.g., a human. The reflectedsignal is then received, amplified and mixed with a portion of theoriginal signal, and the output of this mixer is then low pass filtered.The resulting signal contains information about the movement,respiration and cardiac activity of the person, and is referred to asthe raw sensor signal. In an alternative embodiment, the system may alsouse quadrature transmission in which two carrier signals 90 degrees outof phase are used. In the limits that the pulse becomes very short intime, such a system can be characterized as an ultrawideband (UWB)radio-frequency sensor.

FIG. 2 is a diagram illustrating a schematic of how the raw sensorsignal can be processed to produce three signals for further processing.The raw signal generally will contain components reflecting acombination of bodily movement, respiration, and cardiac activity.Bodily movement can be identified by using zero-crossing or energyenvelope detection algorithms (or more complex algorithms), and used toform a “motion on” or “motion off” indicator. The respiratory activityis typically in the range 0.1 to 0.8 Hz, and can be derived by filteringthe original signal with a bandpass filter whose passband is in thatregion. The cardiac activity is reflected in signals at higherfrequencies, and this activity can be accessed by filtering with abandpass filter with a pass band such as 1 to 10 Hz.

FIG. 3 is a diagram illustrating a more detailed view of the means bywhich the raw sensor signal can be processed to yield motioninformation. One technique calculates the energy envelope of the signalover a period of time, and periods which have a high energy envelope bycomparison with a threshold are determined to be periods of motion. Asecond technique counts the number of times the signal crosses athreshold (e.g., the zero value) and areas with a high value ofzero-crossing are determined as being high motion areas. Thesetechniques can be used separately or in combination to achieve a motiondetection.

FIG. 4 is a diagram illustrating sample signals acquired from the systemfor respiratory activity, in comparison with the signals obtained fromthe current clinical gold standard of inductance plethysmography (usinga commercial system called Respiband®). The disclosed apparatus andsystem are capable of measuring both the amplitude and frequency ofbreathing.

FIG. 5 is a diagram illustrating sample signals acquired from theapparatus and system for cardiac activity, in comparison with thesignals obtained from a conventional heart rate monitoring system basedon a pulse oximeter. The disclosed system is capable of acquiringsignals in which individual heart beats can be distinguished.

FIG. 6 is a diagram illustrating techniques by which the apparatus andsystem may calculate heart rate. Cardiac activity causes a pressure waveat the surface of the body called the ballistocardiogram. In some cases(due to a combination of positioning, body type, and distance from thesensor), the cardiac signals will provide a signal in which individualpulses can be clearly seen. In such cases, heart beats will bedetermined by a threshold passing technique (a pulse is associated withthe point where the signal exceeds the threshold). In more complex (buttypical cases), the ballistocardiogram will present a more complex butrepeatable pulse shape. Therefore a pulse shape template can becorrelated with the acquired cardiac signal, and places where thecorrelation is high will be used as the heart beat locations.

FIG. 7 is a diagram illustrating how the invention integratesinformation from the derived motion m(t), respiratory r(t) and cardiacsignals c(t) together to extract meaningful physiologicalclassifications, by using a classifier model. The three streams of dataare segmented into time epochs, and statistical features are generatedfor each epoch. For example, these features might be the signalvariance, spectral components, or peak values, and these are groupedinto vectors Xr, Xn, and Xc. The vectors can then form a single vector Xof features. These features are combined (for example in a linearweighted fashion using α^(T)X) to determine the probability that theepoch corresponds to a certain physiological state (e.g., person asleep,person awake). The classification from epochs can be further combinedwith classification from other epochs to form higher level decisions(such as whether the person is in REM, NONREM, or WAKE states).

FIG. 8 is a diagram illustrating an example of outputs displayed in oneembodiment. A light emitting diode may be used to indicate sleep state(awake or asleep) clearly to a user in the simplest case. The breathingof the subject may be graphically represented by a bank of lights whichturn on and off as the person breathes in and out. For example, all ofthe lights will be off at the point of maximum inspiration, and alllights will be on at the point of maximum expiration. The display mayalso have a light emitting diode to indicate the central apnea alarmcondition. The heart rate (beats per minute) and the breathing rate(breaths per minute) can be indicated in numerical or graphical formaton the display. An indicator of whether the person is moving can also beincluded.

FIG. 9 is a diagram illustrating how the apparatus and system of thisdisclosure can be used in a configuration where the processing anddisplay unit is remote from the sensor unit, and communication betweenthe two is achieved wirelessly.

FIG. 10 is a diagram illustrating how information may be integrated fromthe derived motion m(t), respiratory r(t) and cardiac signals c(t)together to extract an Apnoea-Hypopnea index (AHI) by using a classifiermodel; an algorithm for processing any combination of the breathingsignal, heart-rate and movement signal to form an estimatedApnoea-Hypopnoea index, and FIG. 11 is a diagram illustrating analgorithm for processing any combination of the breathing signal,heart-rate and movement signal to form an estimated AHI including usingonly measured and/or derived respiratory effort of a human subject.

FIG. 12 illustrates an example output of the epoch labels of apneaestimated from the breathing signal from a night time recording of asubject for which the estimated AHI was 2.9 and the expert determinedAHI was 4.

FIG. 13 is a block diagram of another embodiment of the apparatus andsystem of this disclosure illustrating the possible use of auxiliarysensors such as sound, ultrasound, infrared, light, and/or relativehumidity. It also demonstrates in block diagram format, a representativeschematic of a specific embodiment which includes a transceiver, aprocessor, a data logger, a visual display means, an audible indicator,and auxiliary sensors.

In one embodiment, a system includes a sensor unit, which can be placedrelatively close to where the subject is sleeping (e.g., on a bedsidetable) and a monitoring and display unit through which results can beanalyzed, visualized and communicated to the user. The sensor unit andthe display/monitoring unit may be incorporated into a singlestand-alone unit, if required. The unit may contain one or more of thefollowing features: a non-contact motion sensor for detection of generalbodily movement, respiration, and heart rate; a processing capability toderive parameters such as sleep state, breathing rate, heart rate, andmovement; a display capability to provide visual feedback; an auditorycapability to provide acoustic feedback, e.g., a tone whose frequencyvaries with breathing, or an alarm which sounds when no motion isdetected; and a wired or wireless communications capability to transmitacquired data to a separate unit. This separate unit can carry out theprocessing, display and auditory capability mentioned above.

Additional sensing capabilities can be added to the sensor unit,including a sound sensor; a sensor for measuring body temperature from adistance (infrared); and sensors for environment humidity, temperatureand light level.

In one specific embodiment, the motion sensor may include aradio-frequency Doppler sensor, which can be used to transmitradio-frequency energy (typically in the range 100 MHz to 100 GHz), andwhich then uses the reflected received signal to construct a motionsignal. The principle by which this works is that a radio-frequency waves(t)=u(t)cos(2πf _(c) t+θ))  (1)is transmitted from the unit. In this example, the carrier frequency isf_(c), t is time, and Θ is an arbitrary phase angle, and u(t) is a pulseshape. In a continuous wave system, the magnitude of u(t) is always one,and can be omitted from Eq. (1). More generally, the pulse will bedefined as

$\begin{matrix}{{u(t)} = \left\{ \begin{matrix}{1,{t \in \left\lbrack {{{kT}\mspace{14mu}{kT}} + T_{P}} \right\rbrack},{k \in Z}} \\0\end{matrix} \right.} & (2)\end{matrix}$where T is the period width, and T_(p) is the pulse width. WhereT_(p)<<T, this becomes a pulsed continuous wave system. In the extremecase, as T_(p) becomes very short in time, the spectrum of the emittedsignal becomes very wide, and the system is referred to as anultrawideband (UWB) radar or impulse radar. Alternatively, the carrierfrequency of the RF transmitted signal can be varied (chirped) toproduce a so-called frequency modulated continuous wave (FMCW) system.

This radio frequency signal may be generated by a transmitter collocatedwith the sensor using a local oscillator coupled with circuitry forapplying the pulse gating or, with proper control of signal timing, thetransmitter can separate from the receiver/sensor in a so-called“bistatic” configuration. In the FMCW case, a voltage controlledoscillator is used together with a voltage-frequency converter toproduce the RF signal for transmission. The coupling of the RF signal tothe air may be accomplished using an antenna. The antenna can beomnidirectional (transmitting power more-or-less equally in alldirections) or directional (transmitting power preferentially in certaindirections). It may be advantageous to use a directional antenna in thissystem so that transmitted and reflected energy is primarily coming fromone direction. The apparatus, system, and method of this disclosure iscompatible in various embodiments with various types of antenna such assimple dipole antennas, patch antennas, and helical antennas, and thechoice of antenna can be influence by factors such as the requireddirectionality, size, shape, or cost. It should be noted that theapparatus and system can be operated in a manner which has been shown tobe safe for human use. The system has been demonstrated with a totalsystem emitted average power of 1 mW (0 dBm) and lower. The recommendedsafety level for RF exposure is 1 mW/cm2. At a distance of 1 meter froma system transmitting at 0 dBm, the equivalent power density will be atleast 100 times less than this recommended limit.

In all cases, the emitted signal will be reflected off objects thatreflect radio waves (such as the air-body interface), and some of thereflected signal will be received at a receiver, which can be collocatedwith the transmitter, or which can be separate from the transmitter, ina so-called “bistatic” configuration. The received signal and thetransmitted signal can be multiplied together in a standard electronicdevice called a mixer (either in an analog or digital fashion). Forexample, in the CW case, the mixed signal will equalm(t)=γ cos(2πf _(c) t)cos(2πf _(c) t+Φ(t))  (3)where Φ(t) is the path difference of the transmitted and receivedsignals (in the case where the reflection is dominated by a singlereflective object), and γ is the attenuation experienced by thereflected signal. If the reflecting object is fixed, then Φ(t) is fixed,and so is m(t). In the case of interest to us, the reflecting object(e.g., chest) is moving, and m(t) will be time-varying. As a simpleexample, if the chest is undergoing a sinusoidal motion due torespiration:resp(t)=cos(2πf _(m) t)  (4)then the mixed signal will contain a component at F_(m) (as well as acomponent centred at 2F_(c) which can be simply removed by filtering).The signal at the output of the low pass filter after mixing is referredto as the raw sensor signal, and contains information about motion,breathing and cardiac activity.

The amplitude of the raw sensor signal is affected by the mean pathdistance of the reflected signal, leading to detection nulls and peaksin the sensor (areas where the sensor is less or more sensitive). Thiseffect can be minimised by using quadrature techniques in which thetransmitter simultaneously transmits a signal 90 degrees out of phase(the two signals will be referred to as the I and Q components). Thiswill lead to two reflected signals, which can be mixed, leadingeventually to two raw sensor signals. The information from these twosignals can be combined by taking their modulus (or other techniques) toprovide a single output raw sensor signal.

In the UWB case, an alternative method of acquitting a raw sensor signalmay be beneficial. In the UWB case, the path distance to the mostsignificant air-body interface can be determined by measuring the delaybetween the transmitted pulse and peak reflected signal. For example, ifthe pulse width is 1 ns, and the distance form the sensor to the body is0.5 m, then the total time m(τ) elapsed before a peak reflection of thepulse will be 1/(3×108)s=3.33 ns. By transmitting large numbers ofpulses (e.g., a 1 ns pulse every 1 μs) and assuming that the pathdistance is changing slowly, we can derive a raw sensor signal as theaverage of the time delays over that period of time.

In this way, the sensor, e.g., a radio-frequency sensor, can acquire themotion of the chest wall, or more generally the part of the body atwhich the system is aimed. Directional selectivity can be achieved usingdirectional antennas, or multiple RF transmitters. A respiration signalacquired in this way using a pulsed continuous wave system is shown inthe top panel of FIG. 4 . We stress however that a continuous wave, anFMCW, or a UWB radar can also obtain similar signals.

Moreover, since the bulk of the reflected energy is received from thesurface layer of the skin, this motion sensor can also obtain theballistocardiogram, which is the manifestation of the beating of theheart at the surface of the skin due to changes in blood pressure witheach beat. An example of a surface ballistocardiogram obtained with anRF motion sensor is shown in FIG. 5 , together with a referencecardiogram signal from a finger-mounted pulse oximeter. In the receivedsignal from a sleeping subject, the sensor will typically have a mixtureof a respiration and a cardiac signal, as well as having motionartefacts. These various signals can be separated by signal processingusing a variety of techniques including digital filtering techniques(e.g., a linear bandpass filter of bandwidth 2-10 Hz can be used toextract the cardiac signal primarily, while a bandpass filter ofbandwidth 0.15 to 0.6 Hz can extract the respiration component). Moregeneral digital filtering techniques such as adaptive noise cancellationor non-linear filters may also be used. This is schematicallyillustrated in FIG. 2 .

As mentioned above, the received signal can include large motionartifacts. This is due to the fact that the reflected signals from thebody can contain more than one reflection path, and lead to complexsignals (for example if one hand is moving towards the sensor, and thechest is moving away). Such a complex signal in response to upper bodymotion is shown in the raw signal illustrated in FIG. 2 . The receptionof such signals is useful as it can indicate that the upper body is inmotion, which is useful in determining sleep state. The sensor can alsobe used to detect motion signals from the lower part of the body (suchas involuntary leg jerks) which are useful in the diagnosis of sleepdisorders such as Restless Legs Syndrome or Periodic Limb Movements.

In order to improve the qualities of the measured respiration, cardiac,and motion signals, the physical volume from which reflected energy iscollected by the sensor can be restricted using various methods. Forexample, the transmission antenna can be made “directional” (that is, ittransmits more energy in certain directions), as can the receiverantenna. A technique called “time-domain gating” can be used to onlymeasure reflected signals which arise from signals at a certain physicaldistance form the sensor. Frequency domain gating can be used torestrict motions of the reflected object above a certain frequency.

In a simple embodiment of the system, a single antenna will be used,with a single carrier frequency. This antenna will act as both thetransmit and receive antenna. However, in principle, multiple receiveand transmit antennas can be used, as can multiple carrier frequencies.In the case of measurements at multiple frequencies (e.g., at 500 MHzand 5 GHz) the lower frequency can be used to determine large motionsaccurately without phase ambiguity, which can then be subtracted fromthe higher-frequency sensor signals (which are more suited to measuringsmall motion). Using this sensor, the system collects information fromthe person, and uses that to determine breathing, heart rate, and motioninformation.

The additional optional sensors can be incorporated as follows. Theoptional acoustic sensor in the monitoring is a microphone responsive tosound energy in the range 20-10 KHz (for example), and can be used todetermine background noises, and noises associated with sleeping (e.g.snoring). Background noise cancellation techniques can be used toemphasise the person's breathing noise, if necessary. The subject'ssurface temperature can be measured using an infrared device. Otherenvironmental parameters can be collected such as temperature, humidityand light level using known sensor technology. In particular, motionactivity can also be collected from an under-mattress piezoelectricsensor, and this motion signal can then be used as a substitute or tocomplement the motion signal obtained from the radio-frequency sensor.

All of these sensor inputs may be fed into the unit for processing anddisplay purposes, and for possible transmission to a separate unit (themonitoring unit).

The system can then use its processing capability to combine the sensorinputs to provide a number of useful outputs, and to display theseoutputs in a meaningful manner. These steps are carried out in thefollowing manner.

Information about bodily motion is determined in the following way. Ifthe person moves, there will be a corresponding large change in thereceived signal from the non-contact sensor, due to the suddensignificant change in the radio-frequency path length. These “motionevents” can be recognised by comparing the energy of the signal over ashort epoch (typically 0.5 to 5 seconds) with the baseline movement seenby the sensor over a longer period of time (refer to FIG. 3 ). If theenergy in the epoch exceeds a predetermined threshold relative to theproceeding time, then that epoch is judged to be an “activity event” andis marked as such. The amount by which the energy exceeds the thresholdcan be used to weight the amplitude of the activity of the event.Alternatively, motion can be detected by counting“threshold-crossings”—the number of times the signal passes through apreset level. This is also called a zero-crossing technique.

In that way, a motion profile can be built up of the received signal. Bycomparison with a database of previously collected motion profiles, theoverall motion can be classified into categories such as “no motion”,“slight motion” or “large motion.” In this regard, the apparatus,system, and method of this disclosure may find application in physicalsecurity situations to detect living beings through a visually opaquewall, for example.

Information about respiration can be acquired in the following way.Firstly, the frequency of respiration is a useful means ofcharacterising breathing patterns as faster breathing is associated withrespiratory distress (for example). Respiratory frequency can be definedas the number of breaths per minute, e.g., 10 breaths per minute.Moreover, variability in the respiratory frequency can be a usefulindicator of sleep state. Respiratory frequency is more variable inRapid-Eye-Movement (REM) than in non-REM sleep. To calculate respiratoryfrequency, the signal from the respiratory signal (as shown in FIG. 4 )is processed. Respiratory frequency is calculated over a certain timescale (e.g., 10 seconds or 100 seconds) by taking the power spectraldensity estimate of the signal. Conventional techniques for calculatingpower spectral density such as the averaged periodogram may be used. Ifsections of the respiratory signal have been excessively corrupted bymotion, then a technique called Lomb's periodogram may be used, whichcan estimate power spectral density with missing sections of data. Oncethe power spectral density (PSD) has been calculated, the respiratoryfrequency is located by searching for the peak in the PSD in the range0.1 to 0.8 Hz (which is the normal range of human breathingfrequencies). Since adults typically have lower respiratory frequenciesthan infants and young children, the search range can be reduced to 0.1to 0.5 Hz (for example). If the power in the peak exceeds the averagepower in the rest of the band by a certain amount (e.g., at least 50%stronger than background), then we recognise that frequency as therespiratory frequency for the epoch. In that manner, the respiratoryfrequency of each epoch can be calculated over the period ofmeasurement.

The amplitude of the respiration signal is also of importance, and isreflected in the amplitude of the sensor respiration signal. Amplitudevariation is an identifying feature of a sleep disordered breathingcalled Cheyne-Stokes respiration, in which the amplitude of breathingvaries from very shallow to very large over a time scale of typically 60seconds. The current invention can reliably estimate the amplitude ofthe breathing signal over an epoch by taking the square root of thepower at and near the peak of the respiratory power spectral densitydiscussed above. In this way, the variation of amplitudes over epochs oftime can be tracked.

The periodic nature of the patterns in the respiratory signal are alsoimportant as it can indicate the presence of sleep disorder breathing.Obstructive apnea manifests itself as repeated patterns of disruptedbreathing and recovery breaths over time scales of typically 60 seconds.The current disclosure can reliably detect these patterns by calculatinga power spectral density (PSD) of the epochs of the breathing signal andisolating the frequency component in the 0-0.05 Hz bands.

Obstructive apnea may be detected applying a threshold to thesefrequency components and where a component exceeds the threshold then itcan be said with high reliability that obstructive apnea is present. Amore accurate way is to use the frequency component values (or othermeasures derived from the breathing signal) as an input into aclassifier (for example a linear discriminate classifier) which thenoutput the probability of apnea having occurred during the epoch. Anestimated Apnoea-Hypopnea index (AHI) value may be calculated by summingprobabilities for each epoch, dividing by the duration of the recordingto estimate the minutes per hour in apnea. An AHI value may then becalculated by multiplying the minutes-per-hour in apnoea by apredetermined constant.

In addition to the respiratory information, we can also process thecardiac and movement information to enhance the accuracy of the systemin detecting sleep disordered breathing. For example, information fromthe cardiac activity can be used to enhance the classification accuracyof the respiratory based detector of sleep disordered breathing. Usingthe pulse of that time's a set of features are calculated for each epic,which consists of a plurality of the following PSD of the pulse eventtime, the standard deviation of the pulse event times, and the serialcorrelation of the pulse event times. These cardiac activity featuresare processed by a classifier (such as a linear discriminate classifier)to produce a probability of apnea. Further, information from theactivity can be used to determine when the subject was aroused fromsleep by counting the number of movement ethics per epic and processingthis with a linear discriminate classifier to produce a probability ofapnea so as to identify individual apnoeic events.

The three probabilities (or two or more probabilities if the quality ispoor and no features are calculated for one or more of the breathing,cardiac, or movement signals) can be combined using a probabilitycombiner (for example, by averaging the probabilities).

And estimated Apnea-Hypopnoea Index (AHI) value may be calculated byaveraging the combined probabilities for each epic and multiplying bythe number of epochs per hour to estimate the minutes per hour in apnea.An AHI value may then be calculated by multiplying the minutes per hourin apnea by a predetermined linear mapping.

The apparatus and system of this disclosure has been trained to estimatethe AHI using the respiratory, movement, and heart rate data from 125subjects who have undergone a full polysomnogram. The results show thatthe system can distinguish between patients with moderate to severeapnea (AHI>15) from patients free of apnea (AHI<5) with an accuracy ofgreater than 82%.

It is also of importance to sense when respiration is absent (so calledcentral apnea), for example, in monitoring human babies. This can bemeasured by taking the respiratory amplitude measure defined above overan epoch of interest, and if it falls below a certain threshold (whichdetermines the sensitivity), then it is said that respiration is absent.For example, if no respiration is present for an epoch of 15 seconds inbabies, then an alarm can be sounded to alert the user to the centralapnea condition.

Information about cardiac activity may be acquired in the following way.The initial “cardiac signal” is acquired through bandpass filtering ofthe raw sensor signal, using a bandpass filter. The resulting signal isthen called the ballistocardiogram. Each contraction of the heart isassociated with a characteristic pulse shape seen at the surface of theskin. Each pulse shape can then be determined using a simple techniquesuch as peak finding, or through a more elaborate template matchingapproach. In the template matching approach, a template pulse shape(derived from previous recordings) is correlated with theballistocardiogram. The points at which the correlation is highest aredetermined to be the pulse event times.

The heart rate can then be determined by counting the number of pulseshapes per unit time. Other useful parameters such as inter-cardiacintervals can be determined by calculating the difference between pulseshape times. For example, if the pulse shape times are [0.1 s, 1.1 s,2.3 s, 3.1 s, . . . ] then the corresponding inter-cardiac intervals aregiven by 1 s, 1.2 s, and 0.8 s.

As well as determining respiration rate and amplitude, cardiac rate, andmotion, the system provides for means to combine signals for calculationof further useful outputs. For example, the system can be sued todetermine whether a person is asleep or not over a defined epoch ofmeasurement. The means for doing so is as follows.

Data from the respiration, cardiac and motion channels is segmented intoepochs of time. For example, an epoch might consist of readings over 5seconds or over 5 minutes, depending on the desired configuration. Foreach epoch, a set of features are calculated, which may include one ormore of the following conventionally known and determined features: thecount of activities; the mean amplitude of activity counts; the varianceof activity counts; the dominant respiratory frequency; the respiratorypower (e.g., the integral of the PSD in a region about the dominantrespiratory frequency); the heart rate; the variability of the heartrate; the spectrum of the respiration signal; and the spectrum of theraw signal.

Selected features may be fed into a classifier model (such as aconventional linear discriminant analysis classifier) which will thenprovide the probability for that epoch to belong to a certain class ofinterest. As a specific example, three classes are known and defined inthe art for sleep state: AWAKE, NON-REM SLEEP, REM SLEEP. Each of theseclasses may be associated in a probabilistic sense with a preferreddistribution of feature values, and the classifier model uses thisstatistical fact to provide a classification output for each epoch.Moreover, probabilities from each epoch can be further combined toenhance the accuracy of the classification. These epoch classificationscan then be combined over an entire night's recording to provide aso-called hypnogram, which maps the time period into different sleepstages. An important parameter that can be derived from the hypnogram isthe sleep efficiency, which is the percentage of time asleep as afraction of the total time in bed.

The information can also provide a measure of sleep quality by measuringmotion over the night's sleep. As above, the data is divided into epochsof time, and activity count features are measured over each epoch. Basedon comparison with a previously collected database, and using theclassifier methodology outlined above, each epoch can then be classed as“no motion”, “gentle motion”, “moderate motion” or “severe motion”. Fromthese epoch classifications, a sleep quality index can be determined bycounting the number of epochs assigned to each motion class.

The apparatus, system, and method of this disclosure can also be used toprovide information about the transition between non-REM (rapid eyemovement) sleep and REM sleep, as such transitions are known totypically be accompanied by positional changes and relatively largemovements, after a period of relatively low motion.

Further, the apparatus, system, and method of this disclosure can alsobe used to provide a respirogram over the night's recording in a muchless intrusive and complicated manner than is conventionally available.The respirogram is a measure of respiratory frequency over the night'ssleep, and can be calculated by plotting the respiratory frequency overthe entire night's recording.

Discussion of Various Embodiments

Various embodiments of an apparatus, system, and method of physiologicalmonitoring are contemplated by this disclosure. In one embodiment, anapparatus useful in detecting, analyzing, and displaying one or more ofa respiration parameter, cardiac activity, and bodily function ormovement of a subject includes a processor configured to analyze asignal reflected from the subject without physical contact with thesubject and to derive measurements of various physiological parametersof the subject, e.g., a human subject. A display may be configured toprovide the analyzed and derived measurements to a local or remote userof the apparatus. The reflected signal can be an RF signal, or it may beanother type of signal, e.g., ultrasound, infrared, and/or visiblelight.

In another aspect of this and various embodiments, a sensor may becoupled to the processor and arranged to receive the signal reflectedfrom the subject. The sensor and processor are arranged to operatewithout any direct or indirect physical contact with the subject. Inanother aspect of this embodiment, the reflected signal may be generatedby a transmitter collocated with the apparatus. Further, the transmittermay be configured to generate an RF energy signal compatible for usewith a human subject. In still another aspect of this and variousembodiments, a multiplier circuit may be arranged to multiply thereflected signal with a transmitted signal and to output a basebandsignal representing respiration, cardiac, and a bodily function ormovement therefrom. Bodily functions may include, for example, urinationof a child which may be detected by small bodily movements of thesubject.

In another aspect of this and various embodiments, the processor may beconfigured to recognize periods of bodily motion of the human subject byidentifying peaks in an energy envelope of the baseband signal. Further,the processor may be configured to recognize periods of bodily motion ofthe human subject by counting a number of threshold-crossings of thebaseband signal per unit time. In another aspect of this and variousembodiments, a sensor is provided and the processor is configured toreceive the baseband signal and to output a processed signal inresponse, and the processor may further be configured to use theprocessed signal to distinguish breathing activity of the human subjector cardiac activity of the human subject. The processed signal may bethe output of bandpass, multi bandpass, or signal separation processesimplemented by known digital signal processing techniques, for example,by independent component analysis.

In another aspect of this and various embodiments, the processor may beconfigured to determine an activity count for a measurement epoch bycalculating an energy of the baseband signal relative to other epochs.Further, the processor may be configured to run a classifier model so asto determine a Cheyne-Stokes respiration pattern by processing featuresobtained from a respiratory signal derived from the baseband signal. Inaddition, the processor may be configured to determine anApnoea-Hypopnoea Index (AHI) by processing a respiratory signal derivedfrom the baseband signal; the AHI may be determined solely by a derivedrespiratory effort parameter. In a related aspect of this and variousembodiments, the processor may be configured to determine a the sleepingstatus of the subject by analysis of a motion signal derived from thebaseband signal. In other aspects, the classifier model may be run todetermine a sleep state by combining one or more of motion signals,breathing signals, and cardiac signals provided by the classifier model.In a further related aspect of this and various embodiments, theprocessor may be configured to recognize a central apnea condition bydetermining that breathing and motion activity of the subject are belowa predetermined threshold for a period of time. In further aspects, theprocessor may be configured to recognize a respiratory distresscondition of the human subject by comparing a derived respiratoryfrequency with an existing set of respiratory measurements.

In other aspects of the embodiment, the processor causes a visual oraural indication device to signal one or more of a sleep status, abreathing parameter, a heart rate, or a bodily movement of the subjectto a user.

In another embodiment, a system for measuring, analyzing, and displayingone or more of a respiration parameter, cardiac activity, and bodilymovement or function of a subject includes, inter alia, a transmitterarrangement configured to propagate a radio frequency signal toward thesubject and a receiver arranged to receive the signal reflected from thesubject. A processor is arranged to analyze the reflected signal toproduce measurements of one or more of a respiration parameter, cardiacactivity, and a bodily movement or function. A monitor may be used toprovide selected information to a local or remote user of the system byeither an audible or visual indication, or both. The system may furtherinclude one or more auxiliary sensors coupled to the processor, e.g.,one or more of an acoustic sensor, temperature sensor, humidity sensor,and a light sensor.

In another embodiment, a method for measuring, analyzing, and displayingone or more physiological parameters of a subject includes, among othersteps, sensing a signal reflected from the subject and processing andanalyzing the reflected signal. The reflected signal may be an RFsignal. One or more physiological parameters pertaining to the subjectare derived. The physiological parameters may include one or more of arespiration parameter, cardiac activity, and bodily movement or functionof the subject. Finally, selected derived information may then be madeavailable to the user, for example, on a display monitor. In otheraspects, an audible alarm may be sounded in response to a determinationthat one or more of the physiological parameters is outside a normallimit. Such physiological parameters may include, for example, anApnoea-Hypopnoea Index (AHI) obtained by analyzing a respiratory signalderived from the reflected radio signal.

In a related embodiment, a computer-readable medium contains computerinstructions thereon which, when executed on a computer, carry out thefunctions of measuring, analyzing, and displaying one or morephysiological parameters of a living subject by processing and analyzinga signal reflected from the living subject; deriving said one or morephysiological parameters pertaining to said living subject, said one ormore physiological parameters comprising one or more of a respirationparameter, cardiac activity, and bodily movement or function of asubject; and making selected derived information available to a user.

In another aspect of this and various embodiments, the reflected signalmay be processed and analyzed by using a baseband signal obtained bymultiplying a transmitted signal by the reflected signal. The basebandsignal may be analyzed with a classifier and an activity count may thenbe determined in response to the classification result. The determinedactivity count to determine said one or more physiological parameters.

Experimental Results

One example of the application of the apparatus, system, and method ofthis disclosure is in the detection and diagnosis of various sleepdisorders.

Background: Actimetry is a widely accepted technology for the diagnosisand monitoring of sleep disorders such as insomnia, circadian sleep/wakedisturbance, and periodic leg movement. In this study we investigated asensitive non-contact biomotion sensor to measure actimetry and compareits performance to wrist-actimetry. A data corpus consisting of twentysubjects (ten normals, ten with sleep disorders) was collected in theunconstrained home environment with simultaneous non-contact sensor andActiWatch® actimetry recordings used as a baseline standard. Theaggregated length of the data was 151 hours. The non-contact sensorsignal was mapped to actimetry using 30 second epochs and the level ofagreement with the ActiWatch® actimetry determined. Across all twentysubjects, the sensitivity and specificity was 79% and 75% respectively.In addition, it was shown that the non-contact sensor can also measurebreathing and breathing modulations. The results of this study indicatethat the non-contact sensor is a highly convenient alternative towrist-actimetry as a diagnosis and screening tool for sleep studies.Furthermore, as the non-contact sensor measures breathing modulations,it can additionally be used to screen for respiratory disturbances insleep caused by sleep apnea and chronic obstructive pulmonary disease(COPD).

Sleep assessment can be based on many different types of signals.Existing methods to measure these signals, include polysomnography(PSG), actigraphy, and sleep diaries. PSG, the “gold standard” for sleepassessment, may be impractical for some applications, particularly forusage in the home. It can be both intrusive and expensive.

Actimetry is a mature technology, developed over the last 25 years. Anactimeter is a wearable motion sensing and data logging device thatrecords the motion data continuously for days, weeks, or even longer.The actimetry monitor is generally placed on the non-dominant wrist,leg, or sometimes the trunk. The digitized actimetry signal can beprocessed on a computer and used to diagnose and monitor sleep disorderssuch as insomnia, circadian sleep/wake disturbance, and periodic legmovement (PLM). Actigraphy is not considered to be as reliable as fullPSG studies for the diagnosis of sleep disorders, but due to suitabilityto record continuously for long periods of time, its convenience and itslow-cost, it is a very useful screening device. It is considered morereliable than patient sleep logs.

A brief description of conventional actimetry technology is given here.A sensitive linear accelerometer is employed to capture movements. Themovement is bandpass filtered (typically 0.25 to 2-3 Hz). Thiseliminates very slow movements and fast human movements such as shiversand involuntary tremors. Voluntary human movements rarely exceed 3-4 Hz.

The motion is transduced into an analog electrical signal and digitized.The movement counts are accumulated over an epoch, the length of whichis generally user programmable. The analog signal can be digitized usingthree methods, a) time above a threshold, b) number of zero crossings,or c) digital integration. The time above threshold method accumulatesthe amount of time the analog signal is above a pre-determined thresholdduring the epoch. An example threshold might be 0.2 g (g=9.8 m/s²). Twoissues with this method are, (a) that there is a saturation effectbecause the signal amplitude above the threshold is ignored and, (b)movement acceleration is not measured.

The zero crossings method counts the number of times that the actimetrysignal level crosses the zero line during an epoch. Three issues withthis method are that, (a) movement amplitude is not captured, (b)movement acceleration is not measured, and, (c) it is susceptible tolarge invalid count readings due to high frequency artifacts. Thedigital integration method samples the analog actimetry signal at a highrate. The area under the curve is then calculated. Both amplitude andacceleration information is captured. The digital integration method hasbeen found to outperform the time above threshold and zero crossingmethods for identifying movement.

Actigraphy is often reported as counts but it is important to stressthat different hardware devices and different actimetry algorithms canproduce very different counts for the same actimetry. Thus, a directcomparison between ActiWatch® actigraphy and actimetry derived from thenon-contact sensor is difficult. An alternative method is to compare thetemporal location of actimetry. This would allow the capture of falsepositives and false negatives.

Non-contact radar technology sensors can monitor respiratory, movement,and even cardiac signals in an un-intrusive manner. Non-contact sensorsoffer a number of advantages over existing technologies in that 1) thereis no contact with the subject, 2) the cost of the sensor is very low,and 3) the sensors are very portable.

Method: Simultaneous actimetry and non-contact sensor recordings wererecorded for twenty subjects consisting of twelve females and eightmales, with a mean age of 46.7 years (SD 21.3). Nine of the subjectswere classified as healthy. For the other eleven subjects, six hadsevere sleep apnea, two had moderate sleep apnea, one had COPD, one hadchildhood obesity, and one suffered from insomnia. The recordings weremade in the unconstrained home environment under a doctor's supervision.

TABLE I DETAILS OF THE SUBJECTS IN THE TEST CORPUS Record Age LengthNumber Years Sex Health Status (hours) 1 36 F Healthy 8.04 2 29 FHealthy 8.33 3 67 F Moderate Sleep Apnea 7.67 4 30 F Healthy 4.38 5 49 MHealthy 6.89 6 30 F Healthy 7.36 7 31 F Healthy 6.11 8 79 F COPD 7.53 98 F Childhood Obesity 8.06 10 23 F Healthy 8.84 11 34 F Healthy 8.74 1230 F Healthy 7.56 13 34 M Moderate Sleep Apnea 6.33 14 69 M Severe SleepApnea 6.72 15 79 F Insomnia 8.19 16 58 M Severe Sleep Apnea 8.02 17 49 MSevere Sleep Apnea 8.16 18 51 M Severe Sleep Apnea 7.82 19 77 M SevereSleep Apnea 7.92 20 72 M Severe Sleep Apnea 7.97

Actimeter (ActiWatch®): The Actiwatch® (registered trademark of MiniMitter Company) is a long-term activity monitoring device used in thisstudy to provide a baseline of activity counts. It is cordless, and datais transferred to the PC via a close proximity RF link. The Actiwatch®contains a sensor capable of detecting acceleration in two planes. It issensitive to 0.01 g, and integrates the degree and speed of motion andproduces an electrical current with varying magnitude. An increaseddegree of speed and motion produces an increase in voltage. The watchconverts this signal and stores it as activity counts. The maximumsampling rate is 32 Hz. For this study, the watch was placed on thenon-dominant wrist and set to record the number of activity countsduring 15 second intervals (epochs).

Non-contact Sensor: The non-contact sensor employed in this study is amulti-channel biomotion sensor employing 5.8 GHz Doppler radar using amodulation system that limits both the maximum and minimum range.Quadrature operation eliminates range-dependent sensing nulls. Thebaseband inphase (I) and quadrature (Q) signals were filtered usinganalog active filters with bandwidths (0.05-1.6)Hz and (1-5)Hz. Theemitted power is very low-less than 10 mW.

Non-contact Sensor Data Logger: The design of the non-contact biomotionlogger used in this study shares some of the benefits of existingactimeters including convenience of use, light weight, portability,cheap, low power usage, non-intrusive, and the capacity to record forseveral days or even for weeks. The data logger manufactured byBiancaMed Ltd. incorporates all of the aforementioned characteristics,and it can be powered by the electric mains or battery. It is astandalone device which records data from an internal non-contact sensorto an SD flash card for easy transfer to a PC for analysis. It iscapable of logging continuously for weeks with standard off-the-shelf SDcards (up to 4 GB), as used in digital cameras. It contains anindependent battery-powered clock which tags the movement data withaccurate time information and digitizes the sensor channels at 50 Hzwith 10-bit resolution. The user places the data logger no more than 1meter from the bed, between 0.25 to 0.5 meters above the height of themattress, and facing towards the torso of the subject. For the detectionof movement (actimetry), positioning of the logger has been found not tobe crucial. For detection of breathing, the data logger is moresensitive to positioning however, experiments show that if placed withinthe above limits, good signals are obtained.

Non-contact to Actimetry Mapping: The I and Q channels were combinedwhen doing breathing analysis, however, for actimetry data, it issufficient to use only one channel (either I or Q). The mapping from thenon-contact sensor to actimetry is carried out as follows:

1) The first stage is a digital band pass filter with passband (1.5,4.6)Hz, stopband (0.7, 4.9)Hz, 3 dB passband, and stopband attenuationof 50 dB; implemented as a 7th order Butterworth filter. This filterattenuates the breathing frequencies, thus emphasizing the movementfrequencies.

2) The respiration signal is then removed with a sort filter.

3) Finally, the signal is thresholded and summed into non-overlappingtwo second bins to give an actimetry count. The two second epochs canthen be downsampled to the appropriate epoch and compared with wristbased actimetry.

Due to varying clock offsets between the ActiWatch® and data logger, theactimetry and non-contact sensor recordings were aligned manually. Afteralignment, the signals were truncated so that only data that wererecorded simultaneously were retained. The length of each aligned andtruncated set of recordings is given in Table 1. The average length is7.53 hours with an aggregated length of 151 hours across all 20recordings.

Performance Measure: The performances measures are epoch based. Theactimetry counts were aggregated into 30 second epochs for both theActiWatch® and the non-contact actimetry. For each epoch, counts greaterthan one were quantized to one and a comparison made between thequantized counts of the ActiWatch® and the non-contact sensor, i.e., thecomparison measures the accuracy of temporal activity location, ratherthan magnitude of the actimetry. Table 2 shows the four possible statesthat can arise when comparing the reference epoch (ActiWatch® actimetry)with the non-contact actimetry epoch, TN, FN, FP, and TP refer to truenegative, false negative, false positive, and true positive,respectively. The sensitivity (the probability that an epoch withactimetry is detected by the non-contact actimetry mapping) is definedas:

${Sensitivity} = \frac{TP}{{TP} + {FN}}$and the specificity (the probability that the an epoch without actimetryis labeled the same by the non-contact actimetry mapping) is defined as:

${Specificty} = \frac{TN}{{TN} + {FP}}$

TABLE II THE FOUR POSSIBLE COMPARATIVE STATES THAT CAN ARISE BETWEENACTIWATCH ® ACTIMETRY AND NON-CONTACT ACTIMETRY, BASED ON QUANTIZEDEPOCH ACTIMETRY COUNTS Non-contact Actimetry 0 1 ActiWatch ® 0 TN FPActimetry 1 FN TP

Results: FIG. 14 provides experimental results from a non-contact sensorrecording for Record Number 2 (top axis) with the actimetry recording onthe bottom axis in which the signals have been aligned and truncated,and in which the middle axis shows the non-contact signal mapped toActiWatch® actimetry. From FIG. 14 , it can be seen that the non-contactand ActiWatch® actimetry agree very well in temporal location and alsoin magnitude. Table III gives the sensitivity and specificity for eachof the twenty comparisons of the noncontact with ActiWatch® actimetry.

TABLE III EPOCH BASED PERFORMANCE MEASURES FOR EACH OF THE RECORDINGSRecord Number TP FN FP TN Sen (%) Spec(%) 1 64 13 107 783 83 88 2 54 3568 845 61 93 3 94 34 329 465 73 59 4 47 3 81 396 94 83 5 75 16 26 711 8296 6 18 37 32 798 33 96 7 97 73 59 506 57 90 8 191 67 97 550 74 85 9 8518 136 729 83 84 10 150 5 152 755 97 83 11 106 13 528 404 89 43 12 33 726 842 83 97 13 35 6 361 360 85 50 14 59 15 71 663 80 90 15 408 54 43191 88 17 16 43 5 72 844 90 92 17 87 20 229 645 81 74 18 155 46 384 35577 48 19 179 38 265 470 82 64 20 208 8 284 458 96 62 Mean 109 26 187 58479 75

Discussion: Across all twenty subjects, the sensitivity and specificitywere 79% and 75% respectively. The non-contact sensor monitors motionover all of the body will thus registers more motion than a singlenon-dominant wrist positioned ActiWatch®. This may explain the lowerspecificity value. The sensor also proved to be very reliable,convenient and non-invasive. There were no signal quality or equipmentset up issues. None of the subjects reported being disturbed by thesensor. The results of this study show that the non-contact sensor canreliably quantify actimetry. Thus, established actimetry based sleepalgorithms can be deployed on non-contact based actimetry data and, forexample, sleep efficiency can be estimated. A full PSG was not carriedout for this study, and hence expert annotated EEG based sleep stagingwas not possible.

Due to the lack of expert sleep staging, the sleep efficiencies from theActiwatch® and non-contact-actimetry were not compared at this time. Ourresults demonstrate that the non-contact sensor can reliably measure thebreathing signal, for example, a spectrogram (not shown) of an overnightnon-contact sensor signal and the breathing frequencies of approximately0.3 Hz (18 breaths per minute) were readily ascertainable. Additionally,a sample non-contact breathing signal taken from a subject with mildsleep apnea provides evidence in the modulations in the breathing signalthat apnea is present, and this shows that the apparatus, system, andmethod of this disclosure, can not only be used as an actimeter, butalso can be employed to automatically screen for respiratorydisturbances during sleep such as occurs during sleep apnea and COPD.

Conclusion: Thus, it has been demonstrated in one example applicationthat non-contact based actigraphy can capture equivalent information tothat of conventional wrist based actigraphy. Furthermore, thenon-contact biomotion sensor is a richer source of physiologicalinformation. Actigraphy is a single modality signal, whereas, thenon-contact biomotion sensor can capture both actigraphy and respirationinformation. The non-contact sensor also proved to be highly convenientand unobtrusive. Even though this demonstration was conducted using anRF signal, other signal types may be used, e.g., ultrasound, infrared,or visible light.

Statement of Industrial Applicability

The apparatus, system and method of this disclosure finds utility innon-invasive, non-contact monitoring and analysis of physiological signsof humans or other living subjects such as respiration and cardiacactivity. This disclosure also has applications to sleep monitoring,stress monitoring, health monitoring, intruder detection, and physicalsecurity.

The invention claimed is:
 1. A system for monitoring a subject, thesystem comprising: a first sensor configured to detect a portion of afirst output signal reflected from the subject, the detected reflectedsignal being processed to obtain a bodily motion signal and one or bothof a respiratory signal and a cardiac signal; and one or more processorsconfigured to: derive a plurality of statistical features for a firsttime period and a second time period, the plurality of derivedstatistical features comprising statistical bodily motion featuresderived from the bodily motion signal, the plurality of derivedstatistical features further comprising one or both of (a) statisticalrespiratory features derived from the respiratory signal, and (b)statistical cardiac features derived from the cardiac signal;selectively combine two or more of the plurality of derived statisticalfeatures to estimate a parameter that provides a measure of one or moreof a sleep disorder, sleep state, or sleep disturbance; and provideoutput representative of physiological information based on one or moreof the plurality of derived statistical features or the estimatedparameter.
 2. The system of claim 1 further comprising a displayconfigured to display at least one of the plurality of derivedstatistical features.
 3. The system of claim 2, wherein the statisticalfeatures comprise any one of signal variance, spectral components, peakvalues, power spectral density (PSD) of an event time, standarddeviation of event times, serial correlation of event times, count ofactivities, mean amplitude of activity counts, variance of activitycounts, dominant respiratory frequency, respiratory power, respiratoryrate, variability of respiratory rate, and a spectrum of the respiratorysignal.
 4. The system of claim 1, wherein the detected reflected signalis processed to obtain the bodily motion signal and the respiratorysignal, and wherein the plurality of derived statistical featurescomprises the statistical respiratory features.
 5. The system of claim4, wherein the one or more processors are further configured tocalculate an amplitude of the respiratory signal, and wherein one ormore of the statistical respiratory features are derived from theamplitude of the respiratory signal.
 6. The system of claim 4, whereinone or more processors are further configured to determine periodicrespiratory patterns by calculating a power spectral density (PSD) ofepochs of the respiratory signal and isolating frequency componentvalues in a predetermined frequency band.
 7. The system of claim 1further comprising at least one additional sensor configured to outputadditional signals indicating one or more environmental parameters, andwherein the one or more processors are further configured to combine atleast one of the environmental parameters with the two or more of theplurality of derived statistical features to estimate the parameter thatprovides a measure of one or more of a sleep disorder, sleep state, orsleep disturbance.
 8. The system of claim 7, wherein the first sensorcomprises a non-contact radio frequency (RF) sensor configured toreceive RF signals reflected off the subject, and wherein the at leastone additional sensor comprises a non-contact acoustic sensor configuredto determine background noises and noises associated with sleeping. 9.The system of claim 7, wherein the first sensor comprises a non-contactradio frequency (RF) sensor configured to receive RF signals reflectedoff the subject, and wherein the at least one additional sensorcomprises a non-contact ultrasound sensor, a non-contact temperaturesensor, a non-contact humidity sensor, or a non-contact light sensor.10. The system of claim 1, wherein the one or more processors arefurther configured to recognize a distress condition of the subject byderiving a set of physiological features based on one or more of theplurality of derived statistical features or the estimated parameter andcomparing the derived set of physiological features with an existing setof physiological features.
 11. The system of claim 1, wherein theestimated parameter provides a measure of sleep state, and wherein sleepstate comprises at least one of awake, non-REM sleep, and REM sleep. 12.The system of claim 1, wherein the one or more processors are furtherconfigured to combine the estimated parameter with other parametersestimated from combinations of derived statistical features for othertime periods.
 13. The system of claim 12, wherein the one or moreprocessors are further configured to provide a hypnogram using theestimated parameters.
 14. The system of claim 13, wherein the hypnogramprovides a mapping of the estimated parameters to a sleep state over aperiod of time.
 15. The system of claim 1, wherein the detectedreflected signal is processed to obtain the bodily motion signal and thecardiac signal, and wherein the plurality of derived statisticalfeatures comprises the statistical cardiac features.
 16. The system ofclaim 15, wherein the statistical cardiac features include a heart ratedetermined from a number of peaks found in the cardiac signal.
 17. Thesystem of claim 15, wherein the one or more processors are furtherconfigured to correlate a pulse shape template with sections of thecardiac signal and derive one or more of the statistical cardiacfeatures from sections of the cardiac signal in which the correlation isgreater than or equal to a predetermined threshold.
 18. The system ofclaim 17, wherein the statistical cardiac features include a heart ratedetermined from a number of sections of the cardiac signal in which thecorrelation is greater than or equal to the predetermined threshold. 19.A method for monitoring a subject, the method comprising: detecting,with a sensor, a portion of a first output signal reflected from thesubject, the detected reflected signal being processed to obtain abodily motion signal and one or both of a respiratory signal and acardiac signal; and deriving, with one or more processors, a pluralityof statistical features for a first time period and a second timeperiod, the plurality of derived statistical features comprisingstatistical bodily motion features derived from the bodily motionsignal, the plurality of derived statistical features further comprisingone or both of (a) statistical respiratory features derived from therespiratory signal, and (b) statistical cardiac features derived fromthe cardiac signal; selectively combining, with the one or moreprocessors, two or more of the plurality of derived statistical featuresto estimate a parameter that provides a measure of one or more of asleep disorder, sleep state, or sleep disturbance; and providing, withthe one or more processors, output representative of physiologicalinformation based on one or more of the plurality of derived statisticalfeatures or the estimated parameter.
 20. A non-transitory computerreadable storage medium containing program instructions for causing atleast one processing device to perform a method for monitoring asubject, the method comprising: obtaining a signal reflected from thesubject using at least one sensor; processing the reflected signal toobtain a bodily motion signal and one or both of a respiratory signaland a cardiac signal; deriving a plurality of statistical features for afirst time period and a second time period, the plurality of derivedstatistical features comprising statistical bodily motion featuresderived from the bodily motion signal, the plurality of derivedstatistical features further comprising one or both of (a) statisticalrespiratory features derived from the respiratory signal, and (b)statistical cardiac features derived from the cardiac signal;selectively combining two or more of the plurality of derivedstatistical features to estimate a parameter that provides a measure ofone or more of a sleep disorder, sleep state or sleep disturbance; andgenerating for output physiological information based on one or more ofthe plurality of derived statistical features or the estimatedparameter.